dr. C.G. Zeinstra (Chris)

Engineering & Materials Science

# Biometrics
# Classifiers
# Face Recognition
# Image Resolution
# Software Packages

Mathematics

# Classifier

Physics & Astronomy

# Confidence
# Intervals

Faculty of Science and Technology (TNW), Advanced Technology (AT)

Ferla, R. , Spreeuwers, L. , & Zeinstra, C. G. (2022). Exploring the GANformer for Face Generation: Investigating the segmentation and smile augmentation potential. In Proceedings of the 2022 Symposium on Information Theory and Signal Processing in the Benelux (pp. 38)

Bunda, S. , Spreeuwers, L. , & Zeinstra, C. (2022). Sub-byte quantization of Mobile Face Recognition Convolutional Neural Networks. In 2022 International Conference of the Biometrics Special Interest Group (BIOSIG) (pp. 1-5). [9897025] (International Conference of the Biometrics Special Interest Group (BIOSIG); Vol. 2022). IEEE. https://doi.org/10.1109/BIOSIG55365.2022.9897025

de Wit, F. F. , Spreeuwers, L. , & Zeinstra, C. G. (2022). Biometric Testing: aligning standards and practice. 26-32. Paper presented at 42nd WIC Symposium on Information Theory and Signal Processing in the Benelux, SITB 2022, Louvain-la Neuve, Belgium.

Bassit, A. , Hahn, F. , Zeinstra, C. , Veldhuis, R. , & Peter, A. (2021). Bloom Filter vs Homomorphic Encryption: Which approach protects the biometric data and satisfies ISO/IEC 24745? In BIOSIG 2021 - Proceedings of the 20th International Conference of the Biometrics Special Interest Group: 15.-17. September 2021 International Digital Conference IEEE. https://doi.org/10.1109/BIOSIG52210.2021.9548304

Zeinstra, C. , Meuwly, D. , Veldhuis, R. , & Spreeuwers, L. (2019). Mind the Gap: A practical framework for classifiers in a forensic context. In 2018 IEEE 9th International Conference on Biometrics Theory, Applications and Systems, BTAS 2018 [8698583] (IEEE nternational Conference on Biometrics Theory, Applications and Systems (BTAS); Vol. 2018). IEEE. https://doi.org/10.1109/BTAS.2018.8698583

Zeinstra, C. , & Haasnoot, E. (2018). Shallow CNNs for the Reliable Detection of Facial Marks. In A. Bromme, A. Uhl, C. Busch, C. Rathgeb, & A. Dantcheva (Eds.), 2018 International Conference of the Biometrics Special Interest Group, BIOSIG 2018 [8553157] (International Conference of the Biometrics Special Interest Group (BIOSIG); Vol. 2018). IEEE. https://doi.org/10.23919/BIOSIG.2018.8553157

Haasnoot, E., Khodabakhsh, A. , Zeinstra, C. , Spreeuwers, L. , & Veldhuis, R. (2018). FEERCI: A Package for Fast Non-Parametric Confidence Intervals for Equal Error Rates in Amortized O(m log n). In A. Bromme, A. Uhl, C. Busch, C. Rathgeb, & A. Dantcheva (Eds.), 2018 International Conference of the Biometrics Special Interest Group, BIOSIG 2018 [8553607] (International Conference of the Biometrics Special Interest Group (BIOSIG); Vol. 2018). IEEE. https://doi.org/10.23919/BIOSIG.2018.8553607

Zeinstra, C. , Veldhuis, R. , & Spreeuwers, L. (2018). Grid-Based Likelihood Ratio Classifiers for the Comparison of Facial Marks. IEEE transactions on information forensics and security, 13(1), 253-264. [8017453]. https://doi.org/10.1109/TIFS.2017.2746013

Zeinstra, C. G. , Meuwly, D., Ruifrok, A. C. C. , Veldhuis, R. N. J. , & Spreeuwers, L. J. (2018). Forensic face recognition as a means to determine strength of evidence: A survey. Forensic Science Review, 30(1), 21-32. http://forensicsciencereview.com/content.htm#v30

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Vakken in het huidig collegejaar worden toegevoegd op het moment dat zij definitief zijn in het Osiris systeem. Daarom kan het zijn dat de lijst nog niet compleet is voor het gehele collegejaar.